Machine Learning for the Prediction of Edge Cracking in Sheet Metal Forming Processes

Abstract

This work aims to evaluate the performance of various machine learning algorithms in the prediction of metal forming defects, particularly the occurrence of edge cracking. To this end, seven different single classifiers and two types of ensemble models (majority voting and stacking) were used to make predictions, based on a dataset generated from the results of two types of mechanical tests: the uniaxial tensile test and the hole expansion test. The performance evaluation was based on four metrics: accuracy, recall, precision and F-score, with the F-score being considered the most relevant. The best performances were achieved by the majority voting models. The ROC curve of a majority voting model was also evaluated, in order to confirm the predictive capabilities of the model. Globally, ML algorithms are able to predict the occurrence of edge cracking satisfactorily.

Machine Learning for Predicting Fracture Strain in Sheet Metal Forming

“…Applications of artificial intelligence (AI) techniques have recently been proposed to predict, detect, and classify the occurrence of defects in metal forming processes [18][19][20][21][22][23][24][25][26][27][28][29]. Among these, there are AI-based techniques that have been used to classify surface defects of hot rolling strips based on techniques such as generative adversarial networks (GAN) [22] and convolutional neural networks (CNN) [23,24], which rely on datasets of collected defects images; CNN-based approaches used to predict the buckling instability of automotive sheet metal panels [25]; machine learning-based techniques used to predict and account for springback in steel and aluminium parts [26][27][28], as well as for predicting wrinkling [29].…”

“…The authors of the current work have previously evaluated the performance of various ML binary classifiers in predicting the occurrence of edge cracking in metal forming processes, exposed to dispersion in the material properties, which yielded satisfactory results [21]. Edge cracking defects refer to the occurrence of fractures in a stamped part, usually at the outer edge of a bent area, where the strain path corresponds to uniaxial tension [21]. In this context, we now seek to evaluate the performance of ML regression algorithms in predicting the occurrence of failure in components obtained from metal forming processes.…”